PROJECT SUMMARY
On-target resistance to small-molecule inhibitor therapy is a major challenge in cancer treatment. While the
design of novel compounds remains an important long-term strategy for obtaining selective and potent inhibition
of these mutants, further tumor evolution and resistance may occur. Treatment with multiple inhibitors that co-
inhibit the same therapeutic target has begun to gain traction as a viable clinical strategy to reduce the
emergence of resistance.
Mesenchymal epithelial transition factor (c-MET/MET), a receptor tyrosine kinase, is an oncogenic driver in many
tumor types, notably non-small cell lung cancer (NSCLC) and melanomas. MET amplification also serves as an
escape mechanism following treatment with EGFR, ALK, or KRASG12C inhibitors, defining MET as a critical
therapeutic target. Some clinically approved kinase inhibitors (KIs) have activity against MET. However,
clinically-observed MET mutations promote resistance to certain KI subtypes that preferentially bind to active or
inactive kinase conformations. Defining which KIs should be used to treat patients presenting with specific MET
mutations is a pressing clinical need.
Combining as few as two or three KIs with shared activity against a target of interest maximizes on-target
selectivity by diluting each inhibitor’s off-target effects. In addition to maximizing on-target effects for MET alone
or MET and therapeutic co-targets, combinations of inhibitors with different binding modes could decrease the
probability of resistance emerging through further mutations.
The objective of this study is to define selectivity profiles for clinically approved KIs against clinically observed
MET mutants, and calculate KI combinations that would most selectively inhibit those mutants. This study will
investigate the hypothesis that combinations of clinically approved KIs can be calculated that will
maximize selectivity for MET mutants or MET/EGFR and MET/ALK, or with sotorasib for MET/KRASG12C
co-targets, and decrease the probability of resistance emerging. This hypothesis will be tested by measuring
the selectivity profiles of KIs for clinically observed MET mutants, predicting KI combinations, and testing
combinations in cells and in a syngeneic orthotopic mouse model of lung adenocarcinoma. The primary rationale
for this work is that it will provide an actionable roadmap for MET mutation-specific targeted KI therapy in clinical
cohorts. Combinations of two or three clinically approved KIs with different binding modes that have been
selected to maximize on-target activity represent an actionable strategy to increase therapeutic potential and
decrease the probability of further KI resistance emerging.